Abstract

As one of the potent greenhouse gases, methane emission from ruminants has been intensively studied over the past decades. Various regression-based models have been applied to examine factors affecting enteric methane emission. Based on Bayesian networks, this paper proposes an alternative network-based approach to model the relationship among factors affecting enteric methane emissions from milking cows. It was evaluated on the dataset consisting of 934 milking dairy cows collected at Agri-Food and Biosciences Institute, Northern Ireland. The preliminary results demonstrated that the proposed model has a great potential to capture the complex relationship among factors and establish causal influence among predictors. To the best of our knowledge, this is the first study to use Bayesian networks to model causal influence among factors associated with enteric methane emission from milking cows.

abstract = "As one of the potent greenhouse gases, methane emission from ruminants has been intensively studied over the past decades. Various regression-based models have been applied to examine factors affecting enteric methane emission. Based on Bayesian networks, this paper proposes an alternative network-based approach to model the relationship among factors affecting enteric methane emissions from milking cows. It was evaluated on the dataset consisting of 934 milking dairy cows collected at Agri-Food and Biosciences Institute, Northern Ireland. The preliminary results demonstrated that the proposed model has a great potential to capture the complex relationship among factors and establish causal influence among predictors. To the best of our knowledge, this is the first study to use Bayesian networks to model causal influence among factors associated with enteric methane emission from milking cows.",

N2 - As one of the potent greenhouse gases, methane emission from ruminants has been intensively studied over the past decades. Various regression-based models have been applied to examine factors affecting enteric methane emission. Based on Bayesian networks, this paper proposes an alternative network-based approach to model the relationship among factors affecting enteric methane emissions from milking cows. It was evaluated on the dataset consisting of 934 milking dairy cows collected at Agri-Food and Biosciences Institute, Northern Ireland. The preliminary results demonstrated that the proposed model has a great potential to capture the complex relationship among factors and establish causal influence among predictors. To the best of our knowledge, this is the first study to use Bayesian networks to model causal influence among factors associated with enteric methane emission from milking cows.

AB - As one of the potent greenhouse gases, methane emission from ruminants has been intensively studied over the past decades. Various regression-based models have been applied to examine factors affecting enteric methane emission. Based on Bayesian networks, this paper proposes an alternative network-based approach to model the relationship among factors affecting enteric methane emissions from milking cows. It was evaluated on the dataset consisting of 934 milking dairy cows collected at Agri-Food and Biosciences Institute, Northern Ireland. The preliminary results demonstrated that the proposed model has a great potential to capture the complex relationship among factors and establish causal influence among predictors. To the best of our knowledge, this is the first study to use Bayesian networks to model causal influence among factors associated with enteric methane emission from milking cows.